Deep neural networks (DNNs) are becoming the first-class solution for autonomous unmanned aerial vehicles (UAVs) applications, especially for tiny, resource-constrained, nano-UAVs, with a few tens of grams in weight and subten centimeters in diameter. DNN visual pipelines have been proven capable of delivering high intelligence aboard nanoUAVs, efficiently exploiting novel multi-core microcontroller units. However, one severe limitation of this class of solutions is the generalization challenge, i.e., the visual cues learned on the specific training domain hardly predict with the same accuracy on different ones. Ultimately, it results in very limited applicability of State-of-the-Art (SoA) autonomous navigation DNNs outside controlled environments. In this work, we tackle this problem in the context of the human pose estimation task with a SoA vision-based DNN [1]. We propose a novel methodology that leverages synthetic domain randomization by applying a simple but effective image background replacement technique to augment our training dataset. Our results demonstrate how the augmentation forces the learning process to focus on what matters most: the pose of the human subject. Our approach reduces the DNN’s mean square error — vs. a non-augmented baseline — by up to 40%, on a never-seen-before testing environment. Since our methodology tackles the DNN’s training stage, the improved generalization capabilities come at zero-cost for the computational/memory burdens aboard the nano-UAV.

We consider a quadrotor equipped with a forward-facing camera, and an user freely moving in its proximity; we control the quadrotor in order to stay in front of the user, using only camera frames. To do so, we train a deep neural network to predict the drone controls given the camera image. Training data is acquired by running a simple hand-designed controller which relies on optical motion tracking data.

We consider the task of controlling a quadrotor to hover in front of a freely moving user, using input data from an onboard camera. On this specific task we compare two widespread learning paradigms: a mediated approach, which learns a high-level state from the input and then uses it for deriving control signals; and an end-to-end approach, which skips high-level state estimation altogether. We show that despite their fundamental difference, both approaches yield equivalent performance on this task. We finally qualitatively analyze the behavior of a quadrotor implementing such approaches.